Bibliographic record
Abstract
Streamflow regime types are identified for the 105 natural Canadian stations using Fuzzy C-Means (FCM) algorithm. The stations are extracted from Reference Hydrometric Basin Network (RHBN, Water Survey of Canada, 2017, http://www.wsc.ec.gc.ca/) for the period of 1966-2010 to classify streams into a set of six overlapping regime types during the common period. These streamflow regime classes include (1) slow-response/warm-season peak (2) fast-response/warm-season peak (3) slow-response/freshet peak (4) fast-response/freshet peak (5) slow-response/cold-season peak (6) fast-response/cold-season peak. Here, we visualize the shapes of annual hydrographs in the six archetype streams during the baseline period of 1966-1975 and show how they evolve to the last decadal period of 2001-2010. More information on how the six flow regime types are derived and a detailed description of each regime type can be found in Zaerpour et al. (2020). Zaerpour, M., Hatami, S., Sadri, J., and Nazemi, A.: A novel algorithmic framework for identifying changing streamflow regimes: Application to Canadian natural streams (1966–2010), Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2020-334, in review, 2020.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".